Chapter 8: The Measurement of Wage Discrimination with Imperfect Information: A Finite Mixture Approach
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Published:2020
Juan Prieto-Rodríguez, Juan Gabriel Rodríguez, Rafael Salas, 2020. "The Measurement of Wage Discrimination with Imperfect Information: A Finite Mixture Approach", Inequality, Redistribution and Mobility, Juan Gabriel Rodríguez, John A. Bishop
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Abstract
Studies on wage discrimination assume that independent observers are able to distinguish a priori which workers are suffering from discrimination. However, this may not be a good assumption when anti-discrimination laws mean that severe penalties can be imposed on discriminatory employers or when unobserved heterogeneity is significant. We develop a wage discrimination model in which workers are not classified a priori. It can be thought of as a generalization of the standard empirical framework, whereas the Oaxaca–Blinder model can be thought of as an extreme case. We propose a finite mixture model to explicitly model unobserved heterogeneity in individual characteristics and estimate the probabilities of being a discriminated or a non-discriminated worker. We illustrate this proposal by estimating wage discrimination in Germany and the UK.
